Identity resolution and data enrichment framework

ABSTRACT

Provided herein are systems and methods for identity resolution and data enrichment. An example method performed by at least one hardware processor includes detecting at an account of a data provider, an update to personally identifiable information (PII). The PII is stored in a source table managed by an account of a data consumer. An identity resolution process is performed based on detecting the update. The identity resolution process includes generating a secure identifier of a user associated with the PII. The method further includes generating at the account of the data provider, a result table including the secure identifier. The result table is shared with the account of the data consumer.

TECHNICAL FIELD

Embodiments of the disclosure relate generally to databases and, morespecifically, to identity resolution and data enrichment framework thatcan be used in a cloud computing platform.

BACKGROUND

Databases are widely used for data storage and access in computingapplications. A goal of database storage is to provide enormous sums ofinformation in an organized manner so that it can be accessed, managed,updated, and shared. In a database, data may be organized into rows,columns, and tables. Different database storage systems may be used forstoring different types of content, such as bibliographic, full text,numeric, and/or image content. Further, in computing, different databasesystems may be classified according to the organizational approach ofthe database. There are many different types of databases, includingrelational databases, distributed databases, cloud databases,object-oriented and others.

Databases are used by various entities and companies for storinginformation that may need to be accessed or analyzed. In an example, aretail company may store a listing of all sales transactions in adatabase. The database may include information about when a transactionoccurred, where it occurred, a total cost of the transaction, anidentifier and/or description of all items that were purchased in thetransaction, and so forth. The same retail company may also store, forexample, client (or user) information (e.g., personally identifiableinformation, or PII) in that same or a different database. Example PIIincludes client names, client contact information, client address, andso forth. Based on different PII usage scenarios, the retail company mayneed to perform identity resolution and data enrichment of the client'sPII. However, existing techniques for performing identity resolution anddata enrichment are time-consuming and challenging to configure andperform securely.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be understood more fully from the detaileddescription given below and from the accompanying drawings of variousembodiments of the disclosure.

FIG. 1 illustrates an example computing environment that includes anetwork-based database system in communication with a cloud storageplatform, in accordance with some embodiments of the present disclosure.

FIG. 2 is a block diagram illustrating the components of a computeservice manager using an identity resolution and enrichment (IRE)manager, in accordance with some embodiments of the present disclosure.

FIG. 3 is a block diagram illustrating components of an executionplatform, in accordance with some embodiments of the present disclosure.

FIG. 4 is a diagram of a stream object configuration for a table, inaccordance with some embodiments of the present disclosure.

FIG. 5 is a diagram of shared views, in accordance with some embodimentsof the present disclosure.

FIG. 6 is a diagram of a stream object based on a complex view, inaccordance with some embodiments of the present disclosure.

FIG. 7 is a block diagram illustrating identity resolution and dataenrichment performed at an account of a data provider, in accordancewith some embodiments of the present disclosure.

FIG. 8 is a block diagram illustrating brokering the exchange of databetween accounts of data consumers using a crosswalk function of a dataprovider, in accordance with some embodiments of the present disclosure.

FIG. 9 is a flow diagram illustrating operations of a database system inperforming a method for identity resolution, in accordance with someembodiments of the present disclosure.

FIG. 10 illustrates a diagrammatic representation of a machine in theform of a computer system within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, in accordance with some embodiments ofthe present disclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to specific example embodiments forcarrying out the inventive subject matter. Examples of these specificembodiments are illustrated in the accompanying drawings, and specificdetails are outlined in the following description to provide a thoroughunderstanding of the subject matter. It will be understood that theseexamples are not intended to limit the scope of the claims to theillustrated embodiments. On the contrary, they are intended to coversuch alternatives, modifications, and equivalents as may be includedwithin the scope of the disclosure.

In the present disclosure, physical units of data that are stored in adata platform—and that make up the content of, e.g., database tables incustomer accounts—are referred to as micro-partitions. In differentimplementations, a data platform may store metadata in micro-partitionsas well. The term “micro-partitions” is distinguished in this disclosurefrom the term “files,” which, as used herein, refers to data units suchas image files (e.g., Joint Photographic Experts Group (JPEG) files,Portable Network Graphics (PNG) files, etc.), video files (e.g., MovingPicture Experts Group (MPEG) files, MPEG-4 (MP4) files, Advanced VideoCoding High Definition (AVCHD) files, etc.), Portable Document Format(PDF) files, documents that are formatted to be compatible with one ormore word-processing applications, documents that are formatted to becompatible with one or more spreadsheet applications, and/or the like.If stored internal to the data platform, a given file is referred toherein as an “internal file” and may be stored in (or at, or on, etc.)what is referred to herein as an “internal storage location.” If storedexternal to the data platform, a given file is referred to herein as an“external file” and is referred to as being stored in (or at, or on,etc.) what is referred to herein as an “external storage location.”These terms are further discussed below.

Computer-readable files come in several varieties, includingunstructured files, semi-structured files, and structured files. Theseterms may mean different things to different people. As used herein,examples of unstructured files include image files, video files, PDFs,audio files, and the like; examples of semi-structured files includeJavaScript Object Notation (JSON) files, eXtensible Markup Language(XML) files, and the like; and examples of structured files includeVariant Call Format (VCF) files, Keithley Data File (KDF) files,Hierarchical Data Format version 5 (HDF5) files, and the like. As knownto those of skill in the relevant arts, VCF files are often used in thebioinformatics field for storing, e.g., gene-sequence variations, KDFfiles are often used in the semiconductor industry for storing, e.g.,semiconductor-testing data, and HDF5 files are often used in industriessuch as the aeronautics industry, in that case for storing data such asaircraft-emissions data. Numerous other examples of unstructured-filetypes, semi-structured-file types, and structured-file types, as well asexample uses thereof, could certainly be listed here as well and will befamiliar to those of skill in the relevant arts. Different people ofskill in the relevant arts may classify types of files differently amongthese categories and may use one or more different categories instead ofor in addition to one or more of these.

As used herein, the term “table” indicates a mutable bag of rows,supporting time travel up to a retention period. As used herein, theterm “view” indicates a named SELECT statement, conceptually similar toa table. In some aspects, a view can be secure, which prevents queriesfrom getting information on the underlying data obliquely. As usedherein, the term “materialized view” indicates a view that is eagerlycomputed rather than lazily (e.g., as a standard view). In some aspects,efficient implementation of materialized views has overlapped withchange tracking functionality. As used herein, the term “stream” refersto a table and a timestamp. In some aspects, a stream may be used toiterate over changes to a table. When a stream is read inside a DataManipulation Language (DML) statement, its timestamp may betransactionally advanced to the greater timestamp of its time interval.

As used herein, the term “identity resolution” refers to the process ofmatching fragments of personally identifiable information (PII) acrossdevices and touchpoints to a single profile, often a person or ahousehold. This profile aids in building a cohesive, multi-channel viewof a consumer. An identity resolution process can generate a secureidentifier (e.g., a secure key) of the person or household. As usedherein, the term “data enrichment” refers to a process of obtainingadditional data related to (and supplementing) an existing set of data(e.g., an existing set of PII).

As used herein, the term “task” indicates an object (e.g., a dataobject) that can execute (e.g., user-managed or managed by anetwork-based database system) any one of the following types of SQLcode: a single SQL statement, a call to a stored procedure, andprocedural logic using scripting.

In some aspects, the disclosed identity resolution and data enrichmentfunctionalities can exist in a network-based database system (e.g., asillustrated in FIGS. 1-3 ) or can be leveraged using an existing API(e.g., via one or more external functions). More specifically, thedisclosed identity resolution and data enrichment techniques allow oneparty (e.g., a data consumer) to share PII data with an identityresolution provider (e.g., a data provider). Example features of thenetwork-based database system which can be used in connection withidentity resolution and data enrichment include configuring and usingsecure functions, data sharing, data streams (also referred to asstreams), and tasks. Such features can work in concert to automate oneor more aspects of the identity resolution and data enrichmentfunctionalities.

The disclosed techniques can be used for configuring an identityresolution and enrichment (IRE) manager to perform identity resolutionand data enrichment functionalities. There are two parties in anidentity resolution process—a data provider and a data consumer (e.g., acustomer/subscriber of services offered by the data provider). The dataconsumer has a data set with PII which needs identity resolution. Thedata provider can provide proprietary functionality that accomplishesidentity resolution for identity information (e.g., PII of a user)submitted from the data consumer. Both the data consumer and the dataprovider can be tenants (or subscribers) of services provided by anetwork-based database system (e.g., services that can include thedisclosed identity resolution and data enrichment functionalities of theIRE manager). In this regard, access to one or more of the disclosedidentity resolution and data enrichment functionalities provided by anIRE manager can be configured (or enabled) in an account of the dataprovider or the data consumer at the network-based database system.

The various embodiments that are described herein are described withreference where appropriate to one or more of the various figures. Anexample computing environment using an IRE manager for configuringidentity resolution and data enrichment functionalities is discussed inconnection with FIGS. 1-3 . Example stream-related configurations whichcan be used with the disclosed identity resolution and data enrichmentfunctions are discussed in connection with FIGS. 4-6 . Example identityresolution and data enrichment frameworks are discussed in connectionwith FIG. 7 , FIG. 8 , and FIG. 9 . A more detailed discussion ofexample computing devices that may be used in connection with thedisclosed techniques is provided in connection with FIG. 10 .

FIG. 1 illustrates an example computing environment 100 that includes adatabase system in the example form of a network-based database system102, in accordance with some embodiments of the present disclosure. Toavoid obscuring the inventive subject matter with unnecessary detail,various functional components that are not germane to conveying anunderstanding of the inventive subject matter have been omitted fromFIG. 1 . However, a skilled artisan will readily recognize that variousadditional functional components may be included as part of thecomputing environment 100 to facilitate additional functionality that isnot specifically described herein. In other embodiments, the computingenvironment may comprise another type of network-based database systemor a cloud data platform. For example, in some aspects, the computingenvironment 100 may include a cloud computing platform 101 with thenetwork-based database system 102, and storage platforms 104 (alsoreferred to as cloud storage platforms). The cloud computing platform101 provides computing resources and storage resources that may beacquired (purchased) or leased (e.g., by data providers and dataconsumers), and configured to execute applications and store data.

The cloud computing platform 101 may host a cloud computing service 103that facilitates storage of data on the cloud computing platform 101(e.g., data management and access) and analysis functions (e.g. SQLqueries, analysis), as well as other processing capabilities (e.g.,performing identity resolution and data enrichment functions describedherein). The cloud computing platform 101 may include a three-tierarchitecture: data storage (e.g., storage platforms 104 and 122), anexecution platform 110 (e.g., providing query processing), and a computeservice manager 108 providing cloud services (e.g., identity resolutionand data enrichment services provided by the IRE manager 130).

It is often the case that organizations that are customers of a givendata platform also maintain data storage (e.g., a data lake) that isexternal to the data platform (i.e., one or more external storagelocations). For example, a company could be a customer of a particulardata platform and also separately maintain storage of any number offiles—be they unstructured files, semi-structured files, structuredfiles, and/or files of one or more other types—on, as examples, one ormore of their servers and/or on one or more cloud-storage platforms suchas AMAZON WEB SERVICES™ (AWS™), MICROSOFT® AZURE®, GOOGLE CLOUDPLATFORM™, and/or the like. The customer's servers and cloud-storageplatforms are both examples of what a given customer could use as whatis referred to herein as an external storage location. The cloudcomputing platform 101 could also use a cloud-storage platform as whatis referred to herein as an internal storage location concerning thedata platform.

From the perspective of the network-based database system 102 of thecloud computing platform 101, one or more files that are stored at oneor more storage locations are referred to herein as being organized intoone or more of what is referred to herein as either “internal stages” or“external stages.” Internal stages are stages that correspond to datastorage at one or more internal storage locations, and where externalstages are stages that correspond to data storage at one or moreexternal storage locations. In this regard, external files can be storedin external stages at one or more external storage locations, andinternal files can be stored in internal stages at one or more internalstorage locations, which can include servers managed and controlled bythe same organization (e.g., company) that manages and controls the dataplatform, and which can instead or in addition include data-storageresources operated by a storage provider (e.g., a cloud-storageplatform) that is used by the data platform for its “internal” storage.The internal storage of a data platform is also referred to herein asthe “storage platform” of the data platform. It is further noted that agiven external file that a given customer stores at a given externalstorage location may or may not be stored in an external stage in theexternal storage location—i.e., in some data-platform implementations,it is a customer's choice whether to create one or more external stages(e.g., one or more external-stage objects) in the customer'sdata-platform account as an organizational and functional construct forconveniently interacting via the data platform with one or more externalfiles.

As shown, the network-based database system 102 of the cloud computingplatform 101 is in communication with the cloud storage platforms 104and 122 (e.g., AWS®, Microsoft Azure Blob Storage®, or Google CloudStorage), client device 114 (e.g., a data provider), and data consumer115 via network 106. The network-based database system 102 is anetwork-based system used for reporting and analysis of integrated datafrom one or more disparate sources including one or more storagelocations within the cloud storage platform 104. The cloud storageplatform 104 comprises a plurality of computing machines and provideson-demand computer system resources such as data storage and computingpower to the network-based database system 102.

The network-based database system 102 comprises a compute servicemanager 108, an execution platform 110, and one or more metadatadatabases 112. The network-based database system 102 hosts and providesdata reporting and analysis services (as well as additional servicessuch as the disclosed identity resolution and data enrichment functions)to multiple client accounts, including an account of the data providerassociated with client device 114 and an account of the data consumer115. In some embodiments, the compute service manager 108 comprises theIRE manager 130 which can configure and provide the identity resolutionand data enrichment functions to accounts of tenants of thenetwork-based database system 102 (e.g., an account of the data providerassociated with client device 114 and an account of the data consumer115). A more detailed description of the identity resolution and dataenrichment functions provided by the IRE manager 130 is provided inconnection with FIGS. 4-9 .

The compute service manager 108 coordinates and manages operations ofthe network-based database system 102. The compute service manager 108also performs query optimization and compilation as well as managingclusters of computing services that provide compute resources (alsoreferred to as “virtual warehouses”). The compute service manager 108can support any number of client accounts such as end-users providingdata storage and retrieval requests, accounts of data providers,accounts of data consumers, system administrators managing the systemsand methods described herein, and other components/devices that interactwith the compute service manager 108.

The compute service manager 108 is also in communication with a clientdevice 114. The client device 114 corresponds to a user of one of themultiple client accounts (e.g., a data provider) supported by thenetwork-based database system 102. The data provider may utilizeapplication connector 128 at the client device 114 to submit datastorage, retrieval, and analysis requests to the compute service manager108 as well as to access other services provided by the compute servicemanager 108 (e.g., identity resolution and data enrichment functions).Client device 114 (also referred to as user device 114) may include oneor more of a laptop computer, a desktop computer, a mobile phone (e.g.,a smartphone), a tablet computer, a cloud-hosted computer, cloud-hostedserverless processes, or other computing processes or devices may beused to access services provided by the cloud computing platform 101(e.g., cloud computing service 103) by way of a network 106, such as theInternet or a private network.

In the description below, actions are ascribed to users, particularlyconsumers and providers. Such actions shall be understood to beperformed concerning client device (or devices) 114 operated by suchusers. For example, a notification to a user may be understood to be anotification transmitted to client device 114, input or instruction froma user may be understood to be received by way of the client device 114,and interaction with an interface by a user shall be understood to beinteraction with the interface on the client device 114. In addition,database operations (joining, aggregating, analysis, etc.) ascribed to auser (consumer or provider) shall be understood to include performingsuch actions by the cloud computing service 103 in response to aninstruction from that user.

In some aspects, a data consumer 115 can communicate with the clientdevice 114 to access functions offered by the data provider.Additionally, the data consumer can access functions (e.g., identityresolution and data enrichment functions) offered by the network-baseddatabase system 102 via the network 106.

The compute service manager 108 is also coupled to one or more metadatadatabases 112 that store metadata about various functions and aspectsassociated with the network-based database system 102 and its users. Forexample, a metadata database 112 may include a summary of data stored inremote data storage systems as well as data available from a localcache. Additionally, a metadata database 112 may include informationregarding how data is organized in remote data storage systems (e.g.,the cloud storage platform 104) and the local caches. Information storedby a metadata database 112 allows systems and services to determinewhether a piece of data needs to be accessed without loading oraccessing the actual data from a storage device.

The compute service manager 108 is further coupled to the executionplatform 110, which provides multiple computing resources (e.g.,execution nodes) that execute, for example, various data storage, dataretrieval, and data processing tasks. The execution platform 110 iscoupled to storage platform 104 and cloud storage platforms 122. Thestorage platform 104 comprises multiple data storage devices 120-1 to120-N. In some embodiments, the data storage devices 120-1 to 120-N arecloud-based storage devices located in one or more geographic locations.For example, the data storage devices 120-1 to 120-N may be part of apublic cloud infrastructure or a private cloud infrastructure. The datastorage devices 120-1 to 120-N may be hard disk drives (HDDs),solid-state drives (SSDs), storage clusters, Amazon S3™ storage systems,or any other data-storage technology. Additionally, the cloud storageplatform 104 may include distributed file systems (such as HadoopDistributed File Systems (HDFS)), object storage systems, and the like.In some embodiments, at least one internal stage 126 may reside on oneor more of the data storage devices 120-1-120-N, and at least oneexternal stage 124 may reside on one or more of the cloud storageplatforms 122.

In some embodiments, communication links between elements of thecomputing environment 100 are implemented via one or more datacommunication networks, such as network 106. These data communicationnetworks may utilize any communication protocol and any type ofcommunication medium. In some embodiments, the data communicationnetworks are a combination of two or more data communication networks(or sub-Networks) coupled with one another. In alternate embodiments,these communication links are implemented using any type ofcommunication medium and any communication protocol.

The compute service manager 108, metadata database(s) 112, executionplatform 110, and storage platform 104, are shown in FIG. 1 asindividual discrete components. However, each of the compute servicemanager 108, metadata database(s) 112, execution platform 110, andstorage platforms 104 and 122 may be implemented as a distributed system(e.g., distributed across multiple systems/platforms at multiplegeographic locations). Additionally, each of the compute service manager108, metadata database(s) 112, execution platform 110, and storageplatforms 104 and 122 can be scaled up or down (independently of oneanother) depending on changes to the requests received and the changingneeds of the network-based database system 102. Thus, in the describedembodiments, the network-based database system 102 is dynamic andsupports regular changes to meet the current data processing needs.

During typical operation, the network-based database system 102processes multiple jobs determined by the compute service manager 108.These jobs are scheduled and managed by the compute service manager 108to determine when and how to execute the job. For example, the computeservice manager 108 may divide the job into multiple discrete tasks andmay determine what data is needed to execute each of the multiplediscrete tasks. The compute service manager 108 may assign each of themultiple discrete tasks to one or more nodes of the execution platform110 to process the task. The compute service manager 108 may determinewhat data is needed to process a task and further determine which nodeswithin the execution platform 110 are best suited to process the task.Some nodes may have already cached the data needed to process the taskand, therefore, be a good candidate for processing the task. Metadatastored in a metadata database 112 assists the compute service manager108 in determining which nodes in the execution platform 110 havealready cached at least a portion of the data needed to process thetask. One or more nodes in the execution platform 110 process the taskusing data cached by the nodes and, if necessary, data retrieved fromthe cloud storage platform 104. It is desirable to retrieve as much dataas possible from caches within the execution platform 110 because theretrieval speed is typically much faster than retrieving data from thecloud storage platform 104.

As shown in FIG. 1 , the cloud computing platform 101 of the computingenvironment 100 separates the execution platform 110 from the storageplatform 104. In this arrangement, the processing resources and cacheresources in the execution platform 110 operate independently of thedata storage devices 120-1 to 120-N in the cloud storage platform 104.Thus, the computing resources and cache resources are not restricted tospecific data storage devices 120-1 to 120-N. Instead, all computingresources and all cache resources may retrieve data from, and store datato, any of the data storage resources in the cloud storage platform 104.

FIG. 2 is a block diagram illustrating components of the compute servicemanager 108, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 2 , the compute service manager 108includes an access manager 202 and a credential management system 204coupled to an access metadata database 206, which is an example of themetadata database(s) 112. Access manager 202 handles authentication andauthorization tasks for the systems described herein. The credentialmanagement system 204 facilitates the use of remotely stored credentials(e.g., credentials stored in one of the remote credential stores) toaccess external resources such as data resources in a remote storagedevice. As used herein, the remote storage devices may also be referredto as “persistent storage devices” or “shared storage devices.” Forexample, the credential management system 204 may create and maintainremote credential store definitions and credential objects (e.g., in theaccess metadata database 206). A remote credential store definitionidentifies a remote credential store (e.g., one or more of the remotecredential stores) and includes access information to access securitycredentials from the remote credential store. A credential objectidentifies one or more security credentials using non-sensitiveinformation (e.g., text strings) that are to be retrieved from a remotecredential store for use in accessing an external resource. When arequest invoking an external resource is received at run time, thecredential management system 204 and access manager 202 use informationstored in the access metadata database 206 (e.g., a credential objectand a credential store definition) to retrieve security credentials usedto access the external resource from a remote credential store.

A request processing service 208 manages received data storage requestsand data retrieval requests (e.g., jobs to be performed on databasedata). For example, the request processing service 208 may determine thedata to process a received query (e.g., a data storage request or dataretrieval request). The data may be stored in a cache within theexecution platform 110 or in a data storage device in storage platform104.

A management console service 210 supports access to various systems andprocesses by administrators and other system managers. Additionally, themanagement console service 210 may receive a request to execute a joband monitor the workload on the system.

The compute service manager 108 also includes a job compiler 212, a joboptimizer 214, and a job executor 216. The job compiler 212 parses a jobinto multiple discrete tasks and generates the execution code for eachof the multiple discrete tasks. The job optimizer 214 determines thebest method to execute the multiple discrete tasks based on the datathat needs to be processed. Job optimizer 214 also handles various datapruning operations and other data optimization techniques to improve thespeed and efficiency of executing the job. The job executor 216 executesthe execution code for jobs received from a queue or determined by thecompute service manager 108.

A job scheduler and coordinator 218 sends received jobs to theappropriate services or systems for compilation, optimization, anddispatch to the execution platform 110. For example, jobs may beprioritized and then processed in that prioritized order. In anembodiment, the job scheduler and coordinator 218 determines a priorityfor internal jobs that are scheduled by the compute service manager 108with other “outside” jobs such as user queries that may be scheduled byother systems in the database but may utilize the same processingresources in the execution platform 110. In some embodiments, the jobscheduler and coordinator 218 identifies or assigns particular nodes inthe execution platform 110 to process particular tasks. A virtualwarehouse manager 220 manages the operation of multiple virtualwarehouses implemented in the execution platform 110. For example, thevirtual warehouse manager 220 may generate query plans for executingreceived queries.

Additionally, the compute service manager 108 includes a configurationand metadata manager 222, which manages the information related to thedata stored in the remote data storage devices and the local buffers(e.g., the buffers in the execution platform 110). The configuration andmetadata manager 222 uses metadata to determine which data files need tobe accessed to retrieve data for processing a particular task or job. Amonitor and workload analyzer 224 oversees processes performed by thecompute service manager 108 and manages the distribution of tasks (e.g.,workload) across the virtual warehouses and execution nodes in theexecution platform 110. The monitor and workload analyzer 224 alsoredistributes tasks, as needed, based on changing workloads throughoutthe network-based database system 102 and may further redistribute tasksbased on a user (e.g., “external”) query workload that may also beprocessed by the execution platform 110. The configuration and metadatamanager 222 and the monitor and workload analyzer 224 are coupled to adata storage device 226. The data storage device 226 in FIG. 2represents any data storage device within the network-based databasesystem 102. For example, data storage device 226 may represent buffersin execution platform 110, storage devices in storage platform 104, orany other storage device.

As described in embodiments herein, the compute service manager 108validates all communication from an execution platform (e.g., theexecution platform 110) to validate that the content and context of thatcommunication are consistent with the task(s) known to be assigned tothe execution platform. For example, an instance of the executionplatform executing a query A should not be allowed to request access todata-source D (e.g., data storage device 226) that is not relevant toquery A. Similarly, a given execution node (e.g., execution node 302-1may need to communicate with another execution node (e.g., executionnode 302-2), and should be disallowed from communicating with a thirdexecution node (e.g., execution node 312-1) and any such illicitcommunication can be recorded (e.g., in a log or other location). Also,the information stored on a given execution node is restricted to datarelevant to the current query and any other data is unusable, renderedso by destruction or encryption where the key is unavailable.

In some embodiments, the compute service manager 108 further includesthe IRE manager 130 which can configure and provide the identityresolution and data enrichment functions to accounts of tenants of thenetwork-based database system 102 (e.g., an account of the data providerassociated with client device 114 and an account of the data consumer115). A more detailed description of the identity resolution and dataenrichment functions provided by the IRE manager 130 is provided inconnection with FIGS. 4-9 .

FIG. 3 is a block diagram illustrating components of the executionplatform 110, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 3 , the execution platform 110 includesmultiple virtual warehouses, including virtual warehouse 1 (or 301-1),virtual warehouse 2 (or 301-2), and virtual warehouse N (or 301-N). Eachvirtual warehouse includes multiple execution nodes that each include adata cache and a processor. The virtual warehouses can execute multipletasks in parallel by using multiple execution nodes. As discussedherein, the execution platform 110 can add new virtual warehouses anddrop existing virtual warehouses in real-time based on the currentprocessing needs of the systems and users. This flexibility allows theexecution platform 110 to quickly deploy large amounts of computingresources when needed without being forced to continue paying for thosecomputing resources when they are no longer needed. All virtualwarehouses can access data from any data storage device (e.g., anystorage device in the cloud storage platform 104).

Although each virtual warehouse shown in FIG. 3 includes three executionnodes, a particular virtual warehouse may include any number ofexecution nodes. Further, the number of execution nodes in a virtualwarehouse is dynamic, such that new execution nodes are created whenadditional demand is present, and existing execution nodes are deletedwhen they are no longer necessary.

Each virtual warehouse is capable of accessing any of the data storagedevices 120-1 to 120-N shown in FIG. 1 . Thus, the virtual warehousesare not necessarily assigned to a specific data storage device 120-1 to120-N and, instead, can access data from any of the data storage devices120-1 to 120-N within the cloud storage platform 104. Similarly, each ofthe execution nodes shown in FIG. 3 can access data from any of the datastorage devices 120-1 to 120-N. In some embodiments, a particularvirtual warehouse or a particular execution node may be temporarilyassigned to a specific data storage device, but the virtual warehouse orexecution node may later access data from any other data storage device.

In the example of FIG. 3 , virtual warehouse 1 includes three executionnodes 302-1, 302-2, and 302-N. Execution node 302-1 includes a cache304-1 and a processor 306-1. Execution node 302-2 includes a cache 304-2and a processor 306-2. Execution node 302-N includes a cache 304-N and aprocessor 306-N. Each execution node 302-1, 302-2, and 302-N isassociated with processing one or more data storage and/or dataretrieval tasks. For example, a virtual warehouse may handle datastorage and data retrieval tasks associated with an internal service,such as a clustering service, a materialized view refresh service, afile compaction service, a storage procedure service, or a file upgradeservice. In other implementations, a particular virtual warehouse mayhandle data storage and data retrieval tasks associated with aparticular data storage system or a particular category of data.

Similar to virtual warehouse 1 discussed above, virtual warehouse 2includes three execution nodes 312-1, 312-2, and 312-N. Execution node312-1 includes a cache 314-1 and a processor 316-1. Execution node 312-2includes a cache 314-2 and a processor 316-2. Execution node 312-Nincludes a cache 314-N and a processor 316-N. Additionally, virtualwarehouse 3 includes three execution nodes 322-1, 322-2, and 322-N.Execution node 322-1 includes a cache 324-1 and a processor 326-1.Execution node 322-2 includes a cache 324-2 and a processor 326-2.Execution node 322-N includes a cache 324-N and a processor 326-N.

In some embodiments, the execution nodes shown in FIG. 3 are statelesswith respect to the data being cached by the execution nodes. Forexample, these execution nodes do not store or otherwise maintain stateinformation about the execution node or the data being cached by aparticular execution node. Thus, in the event of an execution nodefailure, the failed node can be transparently replaced by another node.Since there is no state information associated with the failed executionnode, the new (replacement) execution node can easily replace the failednode without concern for recreating a particular state.

Although each of the execution nodes shown in FIG. 3 includes one datacache and one processor, alternative embodiments may include executionnodes containing any number of processors and any number of caches.Additionally, the caches may vary in size among the different executionnodes. The caches shown in FIG. 3 store, in the local execution node,data that was retrieved from one or more data storage devices in thecloud storage platform 104. Thus, the caches reduce or eliminate thebottleneck problems occurring in platforms that consistently retrievedata from remote storage systems. Instead of repeatedly accessing datafrom the remote storage devices, the systems and methods describedherein access data from the caches in the execution nodes, which issignificantly faster and avoids the bottleneck problem discussed above.In some embodiments, the caches are implemented using high-speed memorydevices that provide fast access to the cached data. Each cache canstore data from any of the storage devices in the cloud storage platform104.

Further, the cache resources and computing resources may vary betweendifferent execution nodes. For example, one execution node may containsignificant computing resources and minimal cache resources, making theexecution node useful for tasks that require significant computingresources. Another execution node may contain significant cacheresources and minimal computing resources, making this execution nodeuseful for tasks that require caching of large amounts of data. Yetanother execution node may contain cache resources providing fasterinput-output operations, useful for tasks that require fast scanning oflarge amounts of data. In some embodiments, the cache resources andcomputing resources associated with a particular execution node aredetermined when the execution node is created, based on the expectedtasks to be performed by the execution node.

Additionally, the cache resources and computing resources associatedwith a particular execution node may change over time based on changingtasks performed by the execution node. For example, an execution nodemay be assigned more processing resources if the tasks performed by theexecution node become more processor-intensive. Similarly, an executionnode may be assigned more cache resources if the tasks performed by theexecution node require a larger cache capacity.

Although virtual warehouses 1, 2, and N are associated with the sameexecution platform 110, virtual warehouses 1, N may be implemented usingmultiple computing systems at multiple geographic locations. Forexample, virtual warehouse 1 can be implemented by a computing system ata first geographic location, while virtual warehouses 2 and n areimplemented by another computing system at a second geographic location.In some embodiments, these different computing systems are cloud-basedcomputing systems maintained by one or more different entities.

Additionally, each virtual warehouse is shown in FIG. 3 as havingmultiple execution nodes. The multiple execution nodes associated witheach virtual warehouse may be implemented using multiple computingsystems at multiple geographic locations. For example, an instance ofvirtual warehouse 1 implements execution nodes 302-1 and 302-2 on onecomputing platform at a geographic location, and execution node 302-N ata different computing platform at another geographic location. Selectingparticular computing systems to implement an execution node may dependon various factors, such as the level of resources needed for aparticular execution node (e.g., processing resource requirements andcache requirements), the resources available at particular computingsystems, communication capabilities of networks within a geographiclocation or between geographic locations, and which computing systemsare already implementing other execution nodes in the virtual warehouse.

Execution platform 110 is also fault-tolerant. For example, if onevirtual warehouse fails, that virtual warehouse is quickly replaced witha different virtual warehouse at a different geographic location.

A particular execution platform 110 may include any number of virtualwarehouses. Additionally, the number of virtual warehouses in aparticular execution platform is dynamic, such that new virtualwarehouses are created when additional processing and/or cachingresources are needed. Similarly, existing virtual warehouses may bedeleted when the resources associated with the virtual warehouse are nolonger necessary.

In some embodiments, the virtual warehouses may operate on the same datain the cloud storage platform 104, but each virtual warehouse has itsexecution nodes with independent processing and caching resources. Thisconfiguration allows requests on different virtual warehouses to beprocessed independently and with no interference between the requests.This independent processing, combined with the ability to dynamicallyadd and remove virtual warehouses, supports the addition of newprocessing capacity for new users without impacting the performanceobserved by the existing users.

FIGS. 4-6 describe aspects of secure sharing and configuring a stream(e.g., a stream on a view or a stream on a table), which can be used bythe IRE manager 130 in connection with identity resolution and dataenrichment functionalities performed in an account of a data consumerand an account of a data provider. The terms “stream” and “streamobject” are used interchangeably.

FIG. 4 is diagram 400 of a stream object configuration for a table, inaccordance with some embodiments of the present disclosure. Referring toFIG. 4 , queries or data processing commands Insert 404, Delete 406, andUpdate 408 are applied to source table 402. As illustrated in FIG. 4 , astream 414 is generated on source table T1 402 at times X1, X2 (after atime interval of 410 from X1), and X3 (after a time interval of 412 fromX2). Additionally, at operation 416, table T2 can be created on streamS1. At operation 418, a stream entry from stream S1 at time X1 isinserted into table T2. At operation 420, a stream entry from stream S1at time X2 is inserted into table T2. In this regard, stream data onsource table 402 can be isolated and stored in a separate table T2 foradditional processing.

As used herein, the term “access control” indicates that customers cancontrol who can access database objects within their organization. Asused herein, the term “data sharing” indicates customers (e.g., dataconsumers or data providers) can grant access to database objects (e.g.,a database table or a view in connection with identity resolution anddata enrichment techniques disclosed herein) to other organizations(e.g., other data providers or other data consumers). In some aspects,any query with a CHANGES clause or a stream may be referred to as achange query. A change query on a view may be defined similarly.

In some embodiments, the IRE manager 130 is configured to providechanges to views (e.g., a stream on views) so that the changes may befurther processed and acted on. More specifically, the IRE manager 130may be configured to provide or process streams on views in connectionwith different use cases, such as shared views (e.g., as discussed inconnection with FIG. 5 ) and complex views (e.g., as discussed inconnection with FIG. 6 ). In some aspects, more than one use case mayapply at a given time.

Shared (secure) views may be used to provide (e.g., a user ororganization) limited access to sensitive data. The consumer of the dataoften wishes to observe changes to the data being shared with them. Someconsiderations implied by this use case include giving the consumervisibility into the shared view's retention period and how to enforcesecure view limitations on change queries.

FIG. 5 is a diagram 500 of shared views, in accordance with someembodiments of the present disclosure. Referring to FIG. 5 , a dataconsumer 502 manages a source table 504 (e.g., a source table with PII).The data consumer 502 can apply different filters to source table 504 togenerate views 506 and 508. For example, data consumer 502 can applydifferent filters to source table 504 so that different PII from thetable is shared with different data providers (e.g., data providers 510and 514) in connection with identity resolution or data enrichment,based on specific privacy requirements of each of the data providers. Inthis regard, view 506 is shared with data provider 510, and view 508 isshared with data provider 514. In some embodiments, IRE manager 130configures streams 512 and 516 on corresponding views 506 and 508 forconsumption by data providers 510 and 514.

The definition of a view can be complex but observing the changes tosuch a view may be useful independently of its complexity. Manuallyconstructing a query to compute those changes may be achieved, but canbe toilsome, error-prone, and suffer from performance issues. In someaspects, a change query on a view may automatically rewrite the viewquery, relieving users of this burden. In some aspects, simple viewscontaining only row-wise operators (e.g., select, project, union all)may be used. In some aspects, complex views that join fact tables with(potentially several) slowly-changing-dimension (DIM) tables may also beused. Other kinds of operators like aggregates, windowing functions, andrecursion may also be used in connection with complex views.

FIG. 6 is a diagram 600 of a stream object based on a complex view, inaccordance with some embodiments of the present disclosure. Referring toFIG. 6 , a complex view 608 may be generated based on source tables 602,604, and 606. In some aspects, source tables 602-606 can includedifferent types of PII and enrichment data for a user (e.g., a customerof a data consumer or a data provider). In some embodiments, the IREmanager 130 configures a stream 610 (e.g., at an account of a dataprovider) based on the complex view 608 of source tables 602, 604, and606.

FIG. 7 is a block diagram 700 illustrating identity resolution and dataenrichment performed at an account of a data provider, in accordancewith some embodiments of the present disclosure. Referring to FIG. 7 ,identity resolution and data enrichment functions can be configured(e.g., by the IRE manager 130) to an account 702 of a data consumer andan account 704 of a data provider. More specifically, both the dataconsumer and the data provider can be customers (e.g., account holders)of the network-based database system 102. In the example of FIG. 7 , thedata provider account 704 is configured to perform identity resolutionand data enrichment, and the data consumer account 702 is provisioned touse such functionalities.

Deployment of the identity resolution framework of FIG. 7 consists ofcreating secure objects and data shares in the data consumer account 702and the data provider account 704. The framework can be flexible enoughto incorporate additional functionality, as required. The framework canbe deployed across two accounts on the same cloud provider and region.In the event the data provider and the data consumer are on differentproviders or regions, one of the parties can replicate theirdata/objects to the other party's provider or region.

In some aspects, the data consumer account 702 is provisioned to use theidentity resolution and data enrichment functions configured for thedata provider account 704. Once provisioned, the account of the dataconsumer can configure a source table 706 (also referred to as aconsumer match table) with PII associated with a user (e.g., a customerof the data consumer or the data provider). The source table 706 caninclude a single record of PII associated with the user.

The data consumer account 702 further generates a secure view 708 of thesource table 706 and a data share 710 to share the PII data with thedata provider account 704. More specifically, a match view 712(corresponding to the shared secure view 708) is configured at the dataprovider account 704. The requesting party (e.g., the data consumeraccount 702) can request the data provider account 704 to enable theidentity resolution and data enrichment functionalities, or suchfunctionalities can be enabled automatically based on the pre-configuredsubscription to services of the data provider.

Once the data consumer account 702 is enabled to use identity resolutionfunctionalities of the data provider account 704, PII can beinserted/updated in the source table 706 (e.g., manually orautomatically) which results in corresponding updates of the PII in thematch view 712.

The data provider account 704 is configured with a stream object 714(which can be user-specific) on the match view 712. In this regard, whenthe match view 712 updates due to changes in the source table 706 (e.g.,new or revised PII associated with the user), the stream object 714detects the updates (e.g., insertions or changes to the source table 706and resulting changes in the secure view 708), which causes execution ofthe scheduled task 716. The scheduled task 716 initiates storedprocedure 718 that orchestrates the processing of each PII record foridentity resolution or data enrichment. More specifically, the storedprocedure 718 can call on internal function 720 to perform identityresolution based on the updated PII detected by the stream object 714 inthe match view 712.

In some embodiments, the stored procedure 718 further maintains a usagetracking and billing database 728 with information associated with theusage of the identity resolution and data enrichment functions by thedata consumer.

During identity resolution, the internal function 720 can match theupdated PII from view 712 with existing identity-related data usingidentity and opt-out graph database 722 to determine a user identity (oridentity associated with a household of the user). The identity andopt-out graph database 722 can include identity-related data for usersand user households, as well as opt-out information associated with suchusers or user households. In some aspects, identity resolution can alsouse identity-related data from external database 723.

During identity resolution, for each user/consumer PII record obtainedvia the match view 712, one or more secure identifiers (e.g., keys) canbe generated by the internal function 720. In some aspects, the internalfunction 720 further encrypts the generated one or more secureidentifiers using a user-specific encryption passphrase. The one or moresecure identifiers associated with the user are stored in a result table730 at the data provider account 704. The result table 730 is sharedback to a corresponding result table 734 at the data consumer account702 via data share 732.

In some embodiments, after identity resolution is performed, theinternal function 720 can further perform data enrichment to generateadditional data (also referred to as enrichment data) for the user (orthe user's household) associated with the one or more secure identifiersgenerated during the identity resolution. More specifically, theinternal function can use the master enrichment database 724 (whichstores enrichment data for users and user households) to obtainenrichment data for the user (or the user's household) associated withthe one or more secure identifiers generated during the identityresolution. In some aspects, the internal function 720 further uses theadditional enrichment data/matching logic 726 to perform data matchingand obtain additional enrichment data (e.g., using one or more databasesof the data provider or one or more external databases the data providerhas access to). The determined enrichment data can be stored inadditional result table 740 via a secure view 738. Enrichment datastored at the additional result table 740 is shared into the resulttable 734 of the data consumer account 702 via data share 732.

In some aspects, identity resolution and data enrichment informationgenerated by the internal function 720 can be further revised (e.g.,edited or not provided to the data consumer account 702) based onopt-out information obtained from the identity and opt-out graphdatabase 722 (e.g., when the user has opted out from sharing of theuser's PII or the user's household PII with third parties).

In some aspects, the data consumer account 702 can be configured with amerge/append function 736, which can be used to merge identityresolution data (as well as enrichment data if available) stored in theresult table 734 with the PII stored in source table 706.

In some embodiments, the data provider account 704 can configure thestream object 714 on a staging table instead of a view. In this regard,the data consumer account 702 can use a task that holds the PII in astaging table 742, where the task can execute at a pre-configuredinterval. The staging table 742 at the data consumer account 702 isshared to a staging table 744 at the data provider account 704 via thedata share 710. The stream object 714 can be associated with the stagingtable 744 at the data provider account 704.

In some embodiments, to ensure continual identity resolution, the dataconsumer account 702 can automate an Extract, Transform, and Load (ETL)or an Extract, Load, and Transform (ELT) process to load data into thestaging table 742 on a desired cadence. In some aspects, if ad-hocidentity resolution is desired, a stored procedure can be configured forloading data into staging table 742 or the source table 706. The storedprocedure can also execute the ETL/ELT process referenced above. In someaspects, if the data provider's identity resolution process requiresadditional data not available at the data provider account 704, the dataprovider can automate an ETL/ELT process to load external data into thedata provider account 704.

FIG. 8 is a block diagram 800 illustrating brokering the exchange ofdata between accounts of data consumers using a crosswalk function of adata provider, in accordance with some embodiments of the presentdisclosure. FIG. 8 illustrates the identity resolution and dataenrichment functionalities associated with the data consumer account 702and the data provider account 704 (as described above in connection withFIG. 7 ).

After identity resolution is performed, the data provider returns asecure key (or a set of keys) that can represent a different componentof user identity information (e.g., user's PII). Once the data provideraccount 704 returns the secure key(s) generated during the identityresolution procedure, the data consumer can use the secure key(s) toaccess additional demographic information (or other enrichment data notavailable at the data consumer account 702) about the user identity oruse the secure key(s) to obtain additional enrichment data from a seconddata consumer account 802 (associated with the same data provideraccount 704) or provide enrichment data to the second data consumeraccount 802 for joining their data sets.

More specifically, the data provider account 704 can detect a request810 for additional PII of the user, with the request 810 communicated bythe data consumer account 702 to the account 802 of the second dataconsumer. The request 810 can include the secure identifier (e.g., thesecure key(s)) of the user. The data provider account (e.g., theinternal function 720 or the crosswalk function 808) can retrieve asecond secure identifier of the user, where the second secure identifieris generated based on a second identity resolution process for theaccount 802 of the second data consumer. More specifically, the secondidentity resolution process is performed at the data provider account704 based on an update of the PII of the user at the account 802 of thesecond data consumer. The crosswalk function 808 of the data provideraccount 704 can be configured to replace the secure identifier inrequest 810 with the second secure identifier to generate a revisedrequest 812. The revised request 812 is forwarded to account 802 of thesecond data consumer. Account 802 can retrieve PII information from thesource table 804 or enrichment data from the result table 806 and mayforward such retrieved data back to the data consumer account 702. Inthis regard, the data provider acts as a “broker” that providesfunctionality (e.g., the internal function 720 and the crosswalkfunction 808) which facilitates the joining of the datasets between dataconsumers. In some aspects, data consumers associated with accounts 702and 802 are customers of the data provider and can be configured in adata clean room (DCR) in connection with the above functionalitiesillustrated in FIG. 8 .

FIG. 9 is a flow diagram illustrating operations of a database system inperforming a method 900 for identity resolution, in accordance with someembodiments of the present disclosure. Method 900 may be embodied incomputer-readable instructions for execution by one or more hardwarecomponents (e.g., one or more processors) such that the operations ofthe method 900 may be performed by components of network-based databasesystem 102, such as components of the compute service manager 108 (e.g.,the IRE manager 130) and/or the execution platform 110 (e.g., whichcomponents may be implemented as machine 1000 of FIG. 10 ). Accordingly,method 900 is described below, by way of example with reference thereto.However, it shall be appreciated that method 900 may be deployed onvarious other hardware configurations and is not intended to be limitedto deployment within the network-based database system 102.

At operation 902, an update to personally identifiable information (PII)is detected at an account of a data provider. For example, the PII isassociated with a user and is stored in a source table 706 managed bythe data consumer account 702. An update to the PII in the source table706 is detected via stream object 714 on match view 712 (associated withshared view 708 of the source table 706).

At operation 904, an identity resolution process is performed based ondetecting the update. For example and as described above in connectionwith FIG. 7 , the identity resolution process is performed by theinternal function 720 of the data provider account 704. The identityresolution process includes generating a secure identifier of the userassociated with the PII.

At operation 906, a result table (e.g., result table 730) including thesecure identifier is generated at the account of the data provider.

At operation 908, the result table is shared with the account of thedata consumer. For example, the result table 730 is shared back to acorresponding result table 734 at the data consumer account 702 via datashare 732.

The disclosed techniques can be used to replace some slower and lesssecure identity resolution and data enrichment methods. Such methods arebased on compiling desired data, writing that data to a flat, delimitedfile, then uploading the file to a secure file transfer protocol (sFTP)location. Once received, the data provider copies the file, processesthe data, then returns an output file to the sFTP location, for therequesting party to download. Once downloaded, the requesting party hasto ingest the results into databases. Advantages of the disclosedidentity resolution and data enrichment techniques over such methodsinclude:

-   -   (a) When both the providing and requesting parties' data is in        the network-based database system, the requesting party can        securely share their data with the provider via secure data        sharing.    -   (b) Data does not need to be extracted and transferred, and the        data remains in each party's account.    -   (c) Data access can be fully revocable. While other technologies        allow for stopping data sharing, such technologies do not allow        for full access revocation. With secure data sharing, the        disclosed techniques can be used to completely remove access to        the data, improving compliance with industry regulations like        the Right to Erasure.    -   (d) Other security features, such as row access policies can be        integrated with the disclosed techniques.    -   (e) The data provider's matching/enrichment logic can be        migrated into the network-based database system using stored        procedures that support multiple languages.    -   (f) The disclosed techniques can also be integrated into a Data        Clean Room (DCR), where DCR parties can join on the results from        identity resolution and data enrichment (e.g., as discussed in        connection with FIG. 8 ).    -   (g) The end-to-end process can be completely automated using        streams and tasks (e.g., as discussed in connection with FIGS.        4-7 ).

FIG. 10 illustrates a diagrammatic representation of a machine 1000 inthe form of a computer system within which a set of instructions may beexecuted for causing the machine 1000 to perform any one or more of themethodologies discussed herein, according to an example embodiment.Specifically, FIG. 10 shows a diagrammatic representation of the machine1000 in the example form of a computer system, within which instructions1016 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1000 to perform any oneor more of the methodologies discussed herein may be executed. Forexample, instructions 1016 may cause machine 1000 to execute any one ormore operations of method 900 (or any other technique discussed herein,for example in connection with FIG. 4 -FIG. 9 ). As another example,instructions 1016 may cause machine 1000 to implement one or moreportions of the functionalities discussed herein. In this way,instructions 1016 may transform a general, non-programmed machine into aparticular machine 1000 (e.g., the compute service manager 108 or a nodein the execution platform 110) that is specially configured to carry outany one of the described and illustrated functions in the mannerdescribed herein. In yet another embodiment, instructions 1016 mayconfigure the compute service manager 108 and/or a node in the executionplatform 110 to carry out any one of the described and illustratedfunctions in the manner described herein.

In alternative embodiments, the machine 1000 operates as a standalonedevice or may be coupled (e.g., networked) to other machines. In anetworked deployment, the machine 1000 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1000 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a smartphone, a mobiledevice, a network router, a network switch, a network bridge, or anymachine capable of executing the instructions 1016, sequentially orotherwise, that specify actions to be taken by the machine 1000.Further, while only a single machine 1000 is illustrated, the term“machine” shall also be taken to include a collection of machines 1000that individually or jointly execute the instructions 1016 to performany one or more of the methodologies discussed herein.

Machine 1000 includes processors 1010, memory 1030, and input/output(I/O) components 1050 configured to communicate with each other such asvia a bus 1002. In some example embodiments, the processors 1010 (e.g.,a central processing unit (CPU), a reduced instruction set computing(RISC) processor, a complex instruction set computing (CISC) processor,a graphics processing unit (GPU), a digital signal processor (DSP), anapplication-specific integrated circuit (ASIC), a radio-frequencyintegrated circuit (RFIC), another processor, or any suitablecombination thereof) may include, for example, a processor 1012 and aprocessor 1014 that may execute the instructions 1016. The term“processor” is intended to include multi-core processors 1010 that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions 1016 contemporaneously. AlthoughFIG. 10 shows multiple processors 1010, the machine 1000 may include asingle processor with a single core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiple cores, or any combinationthereof.

The memory 1030 may include a main memory 1032, a static memory 1034,and a storage unit 1036, all accessible to the processors 1010 such asvia the bus 1002. The main memory 1032, the static memory 1034, and thestorage unit 1036 store the instructions 1016 embodying any one or moreof the methodologies or functions described herein. The instructions1016 may also reside, completely or partially, within the main memory1032, within the static memory 1034, within machine storage medium 1038of the storage unit 1036, within at least one of the processors 1010(e.g., within the processor's cache memory), or any suitable combinationthereof, during execution thereof by the machine 1000.

The I/O components 1050 include components to receive input, provideoutput, produce output, transmit information, exchange information,capture measurements, and so on. The specific I/O components 1050 thatare included in a particular machine 1000 will depend on the type ofmachine. For example, portable machines such as mobile phones willlikely include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 1050 mayinclude many other components that are not shown in FIG. 10 . The I/Ocomponents 1050 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 1050 mayinclude output components 1052 and input components 1054. The outputcomponents 1052 may include visual components (e.g., a display such as aplasma display panel (PDP), a light-emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), other signal generators, and soforth. The input components 1054 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touchesgestures or other tactile input components), audio input components(e.g., a microphone), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1050 may include communication components 1064operable to couple the machine 1000 to a network 1080 or device 1070 viaa coupling 1082 and a coupling 1072, respectively. For example, thecommunication components 1064 may include a network interface componentor another suitable device to interface with the network 1080. Infurther examples, the communication components 1064 may include wiredcommunication components, wireless communication components, cellularcommunication components, and other communication components to providecommunication via other modalities. The device 1070 may be anothermachine or any of a wide variety of peripheral devices (e.g., aperipheral device coupled via a universal serial bus (USB)). Forexample, as noted above, machine 1000 may correspond to any one of thecompute service manager 108 or the execution platform 110, and thedevice 1070 may include the client device 114 or any other computingdevice described herein as being in communication with the network-baseddatabase system 102, the storage platform 104, or the cloud storageplatforms 122.

The various memories (e.g., 1030, 1032, 1034, and/or memory of theprocessor(s) 1010 and/or the storage unit 1036) may store one or moresets of instructions 1016 and data structures (e.g., software) embodyingor utilized by any one or more of the methodologies or functionsdescribed herein. These instructions 1016, when executed by theprocessor(s) 1010, cause various operations to implement the disclosedembodiments.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” and “computer-storage medium” mean the same thing and may beused interchangeably in this disclosure. The terms refer to single ormultiple storage devices and/or media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storeexecutable instructions and/or data. The terms shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media, including memory internal or external toprocessors. Specific examples of machine-storage media, computer-storagemedia, and/or device-storage media include non-volatile memory,including by way of example semiconductor memory devices, e.g., erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), field-programmable gate arrays(FPGAs), and flash memory devices; magnetic disks such as internal harddisks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The terms “machine-storage media,” “computer-storage media,” and“device-storage media” specifically exclude carrier waves, modulateddata signals, and other such media, at least some of which are coveredunder the term “signal medium” discussed below.

In various example embodiments, one or more portions of the network 1080may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local-area network (LAN), a wireless LAN (WLAN), awide-area network (WAN), a wireless WAN (WWAN), a metropolitan-areanetwork (MAN), the Internet, a portion of the Internet, a portion of thepublic switched telephone network (PSTN), a plain old telephone service(POTS) network, a cellular telephone network, a wireless network, aWi-Fi® network, another type of network, or a combination of two or moresuch networks. For example, the network 1080 or a portion of the network1080 may include a wireless or cellular network, and the coupling 1082may be a Code Division Multiple Access (CDMA) connection, a GlobalSystem for Mobile communications (GSM) connection, or another type ofcellular or wireless coupling. In this example, the coupling 1082 mayimplement any of a variety of types of data transfer technology, such asSingle Carrier Radio Transmission Technology (1×RTT), Evolution-DataOptimized (EVDO) technology, General Packet Radio Service (GPRS)technology, Enhanced Data rates for GSM Evolution (EDGE) technology,third Generation Partnership Project (3GPP) including 3G,fourth-generation wireless (4G) networks, Universal MobileTelecommunications System (UMTS), High-Speed Packet Access (HSPA),Worldwide Interoperability for Microwave Access (WiMAX), Long TermEvolution (LTE) standard, others defined by various standard-settingorganizations, other long-range protocols, or other data transfertechnology.

The instructions 1016 may be transmitted or received over the network1080 using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components1064) and utilizing any one of several well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, instructions 1016may be transmitted or received using a transmission medium via thecoupling 1072 (e.g., a peer-to-peer coupling) to the device 1070. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure. The terms “transmissionmedium” and “signal medium” shall be taken to include any intangiblemedium that is capable of storing, encoding, or carrying theinstructions 1016 for execution by the machine 1000, and include digitalor analog communications signals or other intangible media to facilitatecommunication of such software. Hence, the terms “transmission medium”and “signal medium” shall be taken to include any form of a modulateddata signal, carrier wave, and so forth. The term “modulated datasignal” means a signal that has one or more of its characteristics setor changed in such a manner as to encode information in the signal.

The terms “machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and transmission media. Thus, the termsinclude both storage devices/media and carrier waves/modulated datasignals.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Similarly, the methods described hereinmay be at least partially processor-implemented. For example, at leastsome of the operations of the disclosed methods may be performed by oneor more processors. The performance of some of the operations may bedistributed among the one or more processors, not only residing within asingle machine but also deployed across several machines. In someexample embodiments, the processor or processors may be located in asingle location (e.g., within a home environment, an office environment,or a server farm), while in other embodiments the processors may bedistributed across several locations.

Described implementations of the subject matter can include one or morefeatures, alone or in combination as illustrated below by way ofexamples.

Example 1 is a system comprising: at least one hardware processor; andat least one memory storing instructions that cause the at least onehardware processor to perform operations comprising: detecting at anaccount of a data provider, an update to personally identifiableinformation (PII), the PII stored in a source table managed by anaccount of a data consumer; performing an identity resolution processbased on detecting the update, the identity resolution processcomprising generating a secure identifier of a user associated with thePII; generating at the account of the data provider, a result tableincluding the secure identifier; and sharing the result table with theaccount of the data consumer.

In Example 2, the subject matter of Example 1 includes subject matterwhere the instructions further cause the at least one hardware processorto perform operations comprising: encrypting the secure identifier withan encryption key associated with the user.

In Example 3, the subject matter of Examples 1-2 includes subject matterwhere the instructions further cause the at least one hardware processorto perform operations comprising: causing at the account of the dataconsumer, a merge of the result table with the source table.

In Example 4, the subject matter of Examples 1-3 includes subject matterwhere the instructions further cause the at least one hardware processorto perform operations comprising: configuring at the account of the dataprovider, a stream object on a view of the source table.

In Example 5, the subject matter of Example 4 includes subject matterwhere the instructions further cause the at least one hardware processorto perform operations comprising: detecting the update to the PII usingthe stream object on the view of the source table.

In Example 6, the subject matter of Examples 1-5 includes subject matterwhere the instructions further cause the at least one hardware processorto perform operations comprising: performing a data enrichment processto obtain enrichment data associated with the user, the enrichment datacomprising additional PII that is supplemental to the PII stored in thesource table.

In Example 7, the subject matter of Example 6 includes subject matterwhere the instructions for performing the data enrichment processfurther cause the at least one hardware processor to perform operationscomprising: retrieving the enrichment data from an enrichment databaseof the data provider based on the secure identifier of the user.

In Example 8, the subject matter of Examples 6-7 includes subject matterwhere the instructions further cause the at least one hardware processorto perform operations comprising: updating the result table with theenrichment data associated with the user.

In Example 9, the subject matter of Examples 1-8 includes subject matterwhere the instructions further cause the at least one hardware processorto perform operations comprising: detecting a request for additional PIIof the user, the request communicated by the account of the dataconsumer to an account of a second data consumer, and the requestincluding the secure identifier of the user.

In Example 10, the subject matter of Example 9 includes subject matterwhere the instructions further cause the at least one hardware processorto perform operations comprising: retrieving a second secure identifierof the user, the second secure identifier generated based on a secondidentity resolution process, and the second identity resolution processperformed at the account of the data provider based on an update of PIIof the user at the account of the second data consumer; replacing thesecure identifier in the request with the second secure identifier togenerate a revised request and forwarding the revised request to theaccount of the second data consumer.

Example 11 is a method comprising: performing by at least one hardwareprocessor: detecting at an account of a data provider, an update topersonally identifiable information (PII), the PII stored in a sourcetable managed by an account of a data consumer; performing an identityresolution process based on detecting the update, the identityresolution process comprising generating a secure identifier of a userassociated with the PII; generating at the account of the data provider,a result table including the secure identifier; and sharing the resulttable with the account of the data consumer.

In Example 12, the subject matter of Example 11 includes, encrypting thesecure identifier with an encryption key associated with the user.

In Example 13, the subject matter of Examples 11-12 includes, causing atthe account of the data consumer, a merge of the result table with thesource table.

In Example 14, the subject matter of Examples 11-13 includes,configuring at the account of the data provider, a stream object on aview of the source table.

In Example 15, the subject matter of Example 14 includes, detecting theupdate to the PII using the stream object on the view of the sourcetable.

In Example 16, the subject matter of Examples 11-15 includes, performinga data enrichment process to obtain enrichment data associated with theuser, the enrichment data comprising additional PII that is supplementalto the PII stored in the source table.

In Example 17, the subject matter of Example 16 includes subject matterwhere performing the data enrichment process further comprises:retrieving the enrichment data from an enrichment database of the dataprovider based on the secure identifier of the user.

In Example 18, the subject matter of Examples 16-17 includes, updatingthe result table with the enrichment data associated with the user.

In Example 19, the subject matter of Examples 11-18 includes, detectinga request for additional PII of the user, the request communicated bythe account of the data consumer to an account of a second dataconsumer, and the request including the secure identifier of the user.

In Example 20, the subject matter of Example 19 includes, retrieving asecond secure identifier of the user, the second secure identifiergenerated based on a second identity resolution process, and the secondidentity resolution process performed at the account of the dataprovider based on an update of PII of the user at the account of thesecond data consumer; replacing the secure identifier in the requestwith the second secure identifier to generate a revised request, andforwarding the revised request to the account of the second dataconsumer.

Example 21 is a computer-storage medium comprising instructions that,when executed by one or more processors of a machine, configure themachine to perform operations comprising: detecting at an account of adata provider, an update to personally identifiable information (PII),the PII stored in a source table managed by an account of a dataconsumer; performing an identity resolution process based on detectingthe update, the identity resolution process comprising generating asecure identifier of a user associated with the PII; generating at theaccount of the data provider, a result table including the secureidentifier; and sharing the result table with the account of the dataconsumer.

In Example 22, the subject matter of Example 21 includes, the operationsfurther comprising: encrypting the secure identifier with an encryptionkey associated with the user.

In Example 23, the subject matter of Examples 21-22 includes, theoperations further comprising: causing at the account of the dataconsumer, a merge of the result table with the source table.

In Example 24, the subject matter of Examples 21-23 includes, theoperations further comprising: configuring at the account of the dataprovider, a stream object on a view of the source table.

In Example 25, the subject matter of Example 24 includes, the operationsfurther comprising: detecting the update to the PII using the streamobject on the view of the source table.

In Example 26, the subject matter of Examples 21-25 includes, theoperations further comprising: performing a data enrichment process toobtain enrichment data associated with the user, the enrichment datacomprising additional PII that is supplemental to the PII stored in thesource table.

In Example 27, the subject matter of Example 26 includes subject matterwhere the operations for performing the data enrichment process furthercomprising: retrieving the enrichment data from an enrichment databaseof the data provider based on the secure identifier of the user.

In Example 28, the subject matter of Examples 26-27 includes, theoperations further comprising: updating the result table with theenrichment data associated with the user.

In Example 29, the subject matter of Examples 21-28 includes, theoperations further comprising: detecting a request for additional PII ofthe user, the request communicated by the account of the data consumerto an account of a second data consumer, and the request including thesecure identifier of the user.

In Example 30, the subject matter of Example 29 includes, the operationsfurther comprising: retrieving a second secure identifier of the user,the second secure identifier generated based on a second identityresolution process, and the second identity resolution process performedat the account of the data provider based on an update of PII of theuser at the account of the second data consumer; replacing the secureidentifier in the request with the second secure identifier to generatea revised request; and forwarding the revised request to the account ofthe second data consumer.

Example 31 is at least one machine-readable medium includinginstructions that, when executed by processing circuitry, cause theprocessing circuitry to perform operations to implement any of Examples1-30.

Example 32 is an apparatus comprising means to implement any of Examples1-30.

Example 33 is a system to implement any of Examples 1-30.

Example 34 is a method to implement any of Examples 1-30.

Although the embodiments of the present disclosure have been describedconcerning specific example embodiments, it will be evident that variousmodifications and changes may be made to these embodiments withoutdeparting from the broader scope of the inventive subject matter.Accordingly, the specification and drawings are to be regarded in anillustrative rather than a restrictive sense. The accompanying drawingsthat form a part hereof show, by way of illustration, and not oflimitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be used and derived therefrom,such that structural and logical substitutions and changes may be madewithout departing from the scope of this disclosure. This DetailedDescription, therefore, is not to be taken in a limiting sense, and thescope of various embodiments is defined only by the appended claims,along with the full range of equivalents to which such claims areentitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is disclosed. Thus, although specific embodiments have beenillustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany adaptations or variations of various embodiments. Combinations ofthe above embodiments, and other embodiments not specifically describedherein, will be apparent to those of skill in the art, upon reviewingthe above description.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended; that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim is still deemed to fall within thescope of that claim.

1. A system comprising: at least one hardware processor; and at leastone memory storing instructions that cause the at least one hardwareprocessor to perform operations comprising: generating, at an account ofa data provider, a view object of personally identifiable information(PII), the PII stored in a source table managed by an account of a dataconsumer and shared with the account of the data provider via a secondview object at the account of the data consumer; detecting using astream object at the account of the data provider, an update to the PIIstored in the view object; performing an identity resolution processbased on detecting the update to the PII stored in the view object, theidentity resolution process comprising generating a secure identifier ofa user associated with the PII; generating at the account of the dataprovider, a result table including the secure identifier; and sharingthe result table with the account of the data consumer.
 2. The system ofclaim 1, wherein the instructions further cause the at least onehardware processor to perform operations comprising: encrypting thesecure identifier with an encryption key associated with the user. 3.The system of claim 1, wherein the instructions further cause the atleast one hardware processor to perform operations comprising: causingat the account of the data consumer, a merge of the result table withthe source table.
 4. The system of claim 1, wherein the instructionsfurther cause the at least one hardware processor to perform operationscomprising: configuring at the account of the data provider, the streamobject to include a plurality of updates of the view object and acorresponding plurality of time stamps associated with times theplurality of updates are obtained.
 5. The system of claim 4, wherein theinstructions further cause the at least one hardware processor toperform operations comprising: initiating the identity resolutionprocess using a scheduled task at the account of the data provider, theinitiating based on the stream object detecting an update of theplurality of updates to the view object.
 6. The system of claim 1,wherein the instructions further cause the at least one hardwareprocessor to perform operations comprising: performing a data enrichmentprocess to obtain enrichment data associated with the user, theenrichment data comprising additional PII that is supplemental to thePII stored in the source table.
 7. The system of claim 6, wherein theinstructions for performing the data enrichment process further causethe at least one hardware processor to perform operations comprising:retrieving the enrichment data from an enrichment database of the dataprovider based on the secure identifier of the user.
 8. The system ofclaim 6, wherein the instructions further cause the at least onehardware processor to perform operations comprising: updating the resulttable with the enrichment data associated with the user.
 9. The systemof claim 1, wherein the instructions further cause the at least onehardware processor to perform operations comprising: detecting a requestfor additional PII of the user, the request communicated by the accountof the data consumer to an account of a second data consumer, and therequest including the secure identifier of the user.
 10. The system ofclaim 9, wherein the instructions further cause the at least onehardware processor to perform operations comprising: retrieving a secondsecure identifier of the user, the second secure identifier generatedbased on a second identity resolution process, and the second identityresolution process performed at the account of the data provider basedon an update of PII of the user at the account of the second dataconsumer; replacing the secure identifier in the request with the secondsecure identifier to generate a revised request; and forwarding therevised request to the account of the second data consumer.
 11. A methodcomprising: performing by at least one hardware processor operationscomprising: generating, at an account of a data provider, a view objectof personally identifiable information (PII), the PII stored in a sourcetable managed by an account of a data consumer and shared with theaccount of the data provider via a second view object at the account ofthe data consumer; detecting using a stream object at the account of thedata provider, an update to the PII stored in the view object;performing an identity resolution process based on detecting the updateto the PII stored in the view object, the identity resolution processcomprising generating a secure identifier of a user associated with thePII; generating at the account of the data provider, a result tableincluding the secure identifier; and sharing the result table with theaccount of the data consumer.
 12. The method of claim 11, furthercomprising: encrypting the secure identifier with an encryption keyassociated with the user.
 13. The method of claim 11, furthercomprising: causing at the account of the data consumer, a merge of theresult table with the source table.
 14. The method of claim 11, furthercomprising: configuring at the account of the data provider, the streamobject to include a plurality of updates of the view object and acorresponding plurality of time stamps associated with times theplurality of updates are obtained.
 15. The method of claim 14, furthercomprising: initiating the identity resolution process using a scheduledtask at the account of the data provider, the initiating based on thestream object detecting an update of the plurality of updates to theview object.
 16. The method of claim 11, further comprising: performinga data enrichment process to obtain enrichment data associated with theuser, the enrichment data comprising additional PII that is supplementalto the PII stored in the source table.
 17. The method of claim 16,wherein performing the data enrichment process further comprises:retrieving the enrichment data from an enrichment database of the dataprovider based on the secure identifier of the user.
 18. The method ofclaim 16, further comprising: updating the result table with theenrichment data associated with the user.
 19. The method of claim 11,further comprising: detecting a request for additional PII of the user,the request communicated by the account of the data consumer to anaccount of a second data consumer, and the request including the secureidentifier of the user.
 20. The method of claim 19, further comprising:retrieving a second secure identifier of the user, the second secureidentifier generated based on a second identity resolution process, andthe second identity resolution process performed at the account of thedata provider based on an update of PII of the user at the account ofthe second data consumer; replacing the secure identifier in the requestwith the second secure identifier to generate a revised request; andforwarding the revised request to the account of the second dataconsumer.
 21. A computer-storage medium comprising instructions that,when executed by one or more processors of a machine, configure themachine to perform operations comprising: generating, at an account of adata provider, a view object of personally identifiable information(PII), the PII stored in a source table managed by an account of a dataconsumer and shared with the account of the data provider via a secondview object at the account of the data consumer; detecting using astream object at the account of the data provider, an update to the PIIstored in the view object; performing an identity resolution processbased on detecting the update to the PII stored in the view object, theidentity resolution process comprising generating a secure identifier ofa user associated with the PII; generating at the account of the dataprovider, a result table including the secure identifier; and sharingthe result table with the account of the data consumer.
 22. Thecomputer-storage medium of claim 21, the operations further comprising:encrypting the secure identifier with an encryption key associated withthe user.
 23. The computer-storage medium of claim 21, the operationsfurther comprising: causing at the account of the data consumer, a mergeof the result table with the source table.
 24. The computer-storagemedium of claim 21, the operations further comprising: configuring atthe account of the data provider, the stream object to include aplurality of updates of the view object and a corresponding plurality oftime stamps associated with times the plurality of updates are obtained.25. The computer-storage medium of claim 24, the operations furthercomprising: initiating the identity resolution process using a scheduledtask at the account of the data provider, the initiating based on thestream object detecting an update of the plurality of updates to theview object.
 26. The computer-storage medium of claim 21, the operationsfurther comprising: performing a data enrichment process to obtainenrichment data associated with the user, the enrichment data comprisingadditional PII that is supplemental to the PII stored in the sourcetable.
 27. The computer-storage medium of claim 26, wherein theoperations for performing the data enrichment process furthercomprising: retrieving the enrichment data from an enrichment databaseof the data provider based on the secure identifier of the user.
 28. Thecomputer-storage medium of claim 26, the operations further comprising:updating the result table with the enrichment data associated with theuser.
 29. The computer-storage medium of claim 21, the operationsfurther comprising: detecting a request for additional PII of the user,the request communicated by the account of the data consumer to anaccount of a second data consumer, and the request including the secureidentifier of the user.
 30. The computer-storage medium of claim 29, theoperations further comprising: retrieving a second secure identifier ofthe user, the second secure identifier generated based on a secondidentity resolution process, and the second identity resolution processperformed at the account of the data provider based on an update of PIIof the user at the account of the second data consumer; replacing thesecure identifier in the request with the second secure identifier togenerate a revised request; and forwarding the revised request to theaccount of the second data consumer.